Breast Mass Classification Using Orthogonal Moments

Abstract

Automatic classification of breast masses in mammograms has been considered a major challenge. Mass shape, margin and density define the malignancy level according to a standardized description, the BI-RADS lexicon. Unlike other approaches, we do not segment masses but instead, we attempt to describe entire regions. In this paper, continuos (Zernike) and discrete (Krawtchouk) orthogonal moments were used to characterize breast masses and their discriminant power to classify benign and malign masses, was assessed. Firstly, Regions of Interest selected by an expert are projected onto two sets of orthogonal polynomials functions, continuous and discrete, thereby drawing shape global information onto a feature space. Using a simple euclidean metric between vectors, the projected images are automatically classified as benign or malign by a k-nearest neighbor strategy. The parameter space is characterized using a set of 150 benign and 150 malign images. The whole method was assessed in a set of 100 masses with different shape and margins and the classification results were compared against a ground truth, already provided by the database. These results showed that discrete Krawtchouk outperformed Zernike moments, reaching an accuracy rate of 90,2% (compared to 81% for Zernike moments), while the area under the curve in a ROC evaluation yielded Az = 0.93 and Az = 0.85 for the Krawtchouk and Zernike strategies, respectively.